by 安全扫描
OpenClaw
安全
high confidenceThe skill's files, instructions, and requirements are coherent with an electricity forecasting framework and do not request disproportionate credentials or install arbitrary third‑party code.
评估建议
This package appears coherent for electricity forecasting. Before running it: (1) review the full scripts (especially deploy_model.py, batch_forecast/send_to_downstream and any truncated functions) to confirm any network endpoints they call; (2) supply API keys only for trusted weather/data providers and store them securely (env vars or secret manager), since example code shows placeholders; (3) run in an isolated virtual environment or container and avoid running as root (batch job writes to /v...详细分析 ▾
✓ 用途与能力
Name/description match the included scripts and reference docs (data prep, training, evaluation, deployment). Nothing in the manifest asks for unrelated cloud credentials, binaries, or config paths.
✓ 指令范围
SKILL.md directs the agent to run local Python scripts, prepare data, train models, and run FastAPI/batch jobs. Instructions reference weather/data APIs as examples (with placeholder API keys) but do not instruct reading unrelated system files or exfiltrating secrets.
✓ 安装机制
No install spec is provided (instruction-only). All code is included in the package; nothing is downloaded from external URLs or extracted to disk during an install step.
✓ 凭证需求
The skill declares no required environment variables or credentials. Example snippets show usage of third‑party APIs that would need user-supplied API keys, which is expected for such data sources and not requested by the skill itself.
✓ 持久化与权限
always:false and normal agent invocation settings. The skill does not request persistent system privileges or attempt to modify other skills or global agent config in the provided files.
⚠ scripts/hyperparameter_search.py:171
Dynamic code execution detected.
⚠ scripts/train_model.py:237
Dynamic code execution detected.
安全有层次，运行前请审查代码。
运行时依赖
无特殊依赖
版本
latestv1.0.02026/4/2
- Initial release of a comprehensive electricity load and demand forecasting framework. - Supports statistical (ARIMA, SARIMA), machine learning (XGBoost, LightGBM, Random Forest), and deep learning models (LSTM, GRU, Transformer, TFT). - Includes tools for data preparation, feature engineering, training, evaluation, backtesting, and deployment. - Provides built-in uncertainty quantification and clear best practices for time series validation and benchmarking. - Detailed documentation and scripts provided for end-to-end forecasting pipelines.
● 无害
安装命令
点击复制官方npx clawhub@latest install electricity-forecasting-framework
镜像加速npx clawhub@latest install electricity-forecasting-framework --registry https://cn.longxiaskill.com